Evaluation of Interpretable Deep Learning Approaches for Neuroimaging in Dementia using Stroke Classification as a Ground Truth for Brain Pathology

使用中风分类作为脑病理学的基本事实来评估痴呆症神经影像的可解释深度学习方法

• 批准号: 2407028
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2407028
• 关键词: Evaluation; Interpretable; Deep; Learning; Approaches

Brief description of the context of the research including potential impactDementia and Alzheimer's disease were the leading causes of death in 2018, with a growing prevalence in the UK that is expected to rise to 1 million by 2025. Deep learning models have shown great promise in aiding early diagnosis and prognosis. However, their complexity and lack of transparency hinders the likelihood of their adoption into clinical practice. This is particularly a problem in clinical routines where stakes are high, and the consequences of misdiagnosis can be life-threatening. In these areas interpretable approaches can provide a way of assessing, and often visualising, the behaviour of the network to better understand why a particular outcome is obtained. However, recent approaches have been limited due to lack of validation and the absence of a ground truth.By taking advantages of the latest technologies from the rapidly developing field of interpretable ML, this project aims to compare alternative interpretability methods in reference to 'ground truth' neurological conditions like stroke before applying them to neurodegenerative disease and dementia.Aims and ObjectivesThis research aims to critically validate the effectiveness of several interpretation methods for dementia diagnosis and prognosis using stroke classification as a ground truth for brain pathology. To begin with, we will aim to build a network that can classify stroke patients from controls with near-perfect accuracy in order to ensure that the attention maps are useful and informative. This will allow us to explore the generalisability of these models to site and/or data quality, voxel dimension and data normalisation. Other technical considerations we will consider include the effect of lesion size or atrophy, and clinical characteristics on model performance and interpretability. Having used the ground-truth case of stroke to assess interpretability approaches, the optimal approaches will then be applied to dementia duringthe PhD phase. The resulting attention maps from these models will be assessed qualitatively and semi-quantitatively for clinical utility by a sample of radiologists (from UCL and collaborating sites) with expertise in dementia.Novelty of Research MethodologyInterpretable medical imaging, a subfield of 'explainable AI', is an important topic for translational research. The planned research on neuroimage interpretability in dementia will be novel and provide both cohort and patient level attention maps, essential for delivering precision medicine for dementia. To our knowledge, comparative assessment of interpretability methods using stroke as a ground truth has not previously been reported. This research includes working closely with radiologists, to provide clinical expertise and data on clinical utility, making it a truly interdisciplinary translational project.Alignment to EPSRC's strategies and research areasOur research focus aligns with EPSRC's strategy for enabling earlier and more effective diagnosis (1) and integration of additional information from clinical data and images using machine learning (3). In addition, collaborating with clinicians aligns with the focus in strong engagement with relevant stakeholders (7). This work falls within the artificial intelligence technologies and image and vision computing research areas.Any companies or collaborators involvedThe project involves collaboration with the UCL Dementia Research Centre (DRC) and the NIHR Biomedical Research Centre at University College London Hospital (UCLH).

研究背景的简要描述，包括潜在的影响痴呆症和阿尔茨海默病是2018年死亡的主要原因，在英国的患病率不断上升，预计到2025年将上升到100万。深度学习模型在帮助早期诊断和预后方面显示出巨大的潜力。然而，它们的复杂性和缺乏透明度阻碍了它们被采用到临床实践中的可能性。这在风险很高的临床常规中尤其是一个问题，并且误诊的后果可能危及生命。在这些领域，可解释的方法可以提供一种评估和可视化网络行为的方法，以更好地理解为什么会获得特定的结果。然而，最近的方法由于缺乏验证和缺乏基础事实而受到限制。通过利用快速发展的可解释ML领域的最新技术，该项目旨在将替代性解释方法应用于神经退行性疾病和痴呆症之前，参考“地面实况”神经系统疾病（如中风），对替代性解释方法进行比较。使用中风分类作为脑病理学基础事实的几种解释方法对痴呆诊断和预后的有效性。开始，我们的目标是建立一个网络，可以分类中风患者从控制接近完美的准确性，以确保注意力地图是有用的和信息。这将使我们能够探索这些模型对研究中心和/或数据质量、体素维度和数据标准化的通用性。我们将考虑的其他技术因素包括病变大小或萎缩的影响，以及临床特征对模型性能和可解释性的影响。在使用中风的真实案例来评估可解释性方法后，最佳方法将在博士阶段应用于痴呆症。由此产生的注意力地图从这些模型将定性和半定量评估的放射科医生（从伦敦大学学院和合作网站）的样本与专业知识在demential.新奇的研究MethodologyInterpretable医学成像，一个子领域的“可解释的人工智能”，是一个重要的课题转化研究的临床效用。计划中的痴呆症神经影像可解释性研究将是新颖的，并提供队列和患者水平的注意力地图，这对于为痴呆症提供精准医学至关重要。据我们所知，以前没有报道过使用中风作为地面实况的可解释性方法的比较评估。该研究包括与放射科医生密切合作，提供临床专业知识和临床实用数据，使其成为真正的跨学科翻译项目。与EPSRC的战略和研究领域保持一致我们的研究重点与EPSRC的战略保持一致，以实现更早和更有效的诊断（1）以及使用机器学习整合临床数据和图像的额外信息（3）。此外，与临床医生合作符合与相关利益相关者密切合作的重点（7）。这项工作福尔斯人工智能技术和图像和视觉计算研究领域。任何公司或合作者参与该项目涉及与伦敦大学学院痴呆症研究中心（DRC）和伦敦大学学院医院（UCLH）的NIHR生物医学研究中心的合作。